MODIS land cover and LAI collection 4 product quality across nine sites in the western hemisphere

Abstract

Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project rotocol S for developing 25-m validation data layers over 49-km study areas. Results from comparisons of MODIS and BigFoot land cover and LA1 products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LA1 products, but there was not a particular MODIS LA1 algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LA1 was generally overpredicted by a significant amount. For evergreen needleleaf sites, LA1 seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addressed.

Sensitivity of the Weather Research and Forecast/Community Multiscale Air Quality modeling system to MODIS LAI, FPAR, and albedo

“…For example, the MODIS Collection 4 LAI/FPAR products tend to be overestimated by around 12% based on the study by Yang et al [2006b], which is similar to the conclusion from the evaluation by Fensholt et al [2004] that MODIS LAI is overestimated by around 2 to 15% and FPAR is overestimated by 8 to 20% on average in a semiarid area. In addition, MODIS LAI could show unrealistic temporal variations during the growing and winter seasons because of cloud/snow contaminations [Cohen et al, 2006]. The MODIS LAI/FPAR algorithm contains a radiative transfer algorithm for the best quality estimates and a backup empirical algorithm based on normalized difference vegetation index-LAI relationships with poor quality for high LAI (saturation) or areas with cloud, aerosol, or snow contamination [Myneni et al, 1997;Yang et al, 2006a].…”

Analysis of Spatio-Temporal Patterns of Leaf Area Index in Different Forest Types of India Using High Temporal Remote Sensing Data

“…Another problem with MODIS estimates are unrealistic day-today variations, which result in very noisy seasonal dynamics. Cohen et al (2006) reported that this was particularly evident during the growing and winter seasons in evergreen needleleaf conifer forests, resulting from cloud and/or snow contamination in the surface reflectance input data. Significantly, it is also stems from the MODIS algorithm taking no account of LAI retrievals from the previous compositing time window.…”

A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

“…In this context, field campaigns and associated high-resolution data, e.g., Landsat-7/8 or Sentinel-2A, provide good source of data to evaluate coarse-resolution products in a multi-temporal manner. Besides, the validation of medium-and coarse-resolution products has been conducted by the scientific community using ground data from different projects such as BigFoot and Validation of Land European Remote Sensing Instruments (VALERI), as well as from the DIRECT 2.0 database [43][44][45][46] hosted at the CEOS cal/val portal (http://calvalportal.ceos.org/), which has recently been updated and is formed of a set of 140 globally-distributed sites where ground measurements were acquired and processed according CEOS LPV guidelines [39,44,46]. Different studies showed that MODIS, CYCLOPES and GEOV1 are consistent global LAI products reaching the Stage 2 validation according to the CEOS hierarchical four-stage validation approach [47][48][49][50].…”